4.7 Article

A full contraction-reaction-diffusion model for pattern formation in geometrically confined microtissues

期刊

APPLIED MATHEMATICAL MODELLING
卷 115, 期 -, 页码 203-220

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2022.10.043

关键词

Biophysics; Embryogenesis; Reaction-diffusion; Active contraction; Poroelasticity; Biphasic material

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Reaction-diffusion models have been used extensively to explain pattern formations in early embryogenesis using geometrically confined microtissues of human pluripotent stem cells. Mechanical cues, such as cellular stresses and strains, play a role in dictating pattern formation during stem cell differentiation. A modified reaction-diffusion model that considers the active contraction of cellular tissues and their poroelastic nature is proposed, where mechanical forces regulate the transport of chemical cues and determine cell fate and pattern formations.
The reaction-diffusion models have been extensively applied to explain the mechanism of pattern formations in early embryogenesis based on geometrically confined microtissues consisting of human pluripotent stem cells. Mechanical cues, such as the cellular stresses and strains, have been found to dictate the pattern formation in human stem cell differentiation. As a result, the traditional reaction-diffusion models are modified by adding mechanically related terms to consider the role played by the mechanical cues during the very early stage of embryogenesis. However, these models either do not consider the activeness of the cellular tissues or neglect their poroelastic nature that biological tissues are made by both cells and interstitial fluid. Here we propose a modified reaction-diffusion model that couples with the active contraction of cellular tissues. The cellular tissue is modelled as a piece of biphasic poroelastic material, where mechanical forces naturally regulate the transport of chemical cues. Such chemical cues direct cell fate and hence yield certain types of pattern formations observed in previous experiments. (c) 2022 Elsevier Inc. All rights reserved.

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